Evidence map›Paper›PMID 38028010›Full record

ReviewHeliyon2023

Metabolomics as a tool for geographic origin assessment of roasted and green coffee beans.

Claudia de León-Solis, Victoria Casasola, Tania Monterroso

Open access · goldAbstract readReview
In one paragraph

Review in Heliyon, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
4.1field-weighted citation impact, top 6% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed, 17 citations in OpenAlex.

  1. Foods (Basel, Switzerland) · 2026
    Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors at 1 institution in 2 countries.

Claudia de León-SolisInstituto de Investigaciones Químicas, Biológicas, Biomédicas y Biofísicas, Mariano Gálvez University, 3 Avenida 9-00 zona 2, 01002, Interior Finca El Zapote, Ciudad de Guatemala, Guatemala.
Victoria CasasolaInstituto de Investigaciones Químicas, Biológicas, Biomédicas y Biofísicas, Mariano Gálvez University, 3 Avenida 9-00 zona 2, 01002, Interior Finca El Zapote, Ciudad de Guatemala, Guatemala.
Tania MonterrosoInstituto de Investigaciones Químicas, Biológicas, Biomédicas y Biofísicas, Mariano Gálvez University, 3 Avenida 9-00 zona 2, 01002, Interior Finca El Zapote, Ciudad de Guatemala, Guatemala.
Universidad Mariano Galvez · GT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Coffee is widely consumed across the globe. The most sought out varieties are Arabica and Robusta which differ significantly in their aroma and taste. Furthermore, varieties cultivated in different regions are perceived to have distinct characteristics encouraging some producers to adopt the denomination of origin label. These differences arise from variations on metabolite content related to edaphoclimatic conditions and post-harvest management among other factors. Although sensory analysis is still standard for coffee brews, instrumental analysis of the roasted and green beans to assess the quality of the final product has been encouraged. Metabolomic profiling has risen as a promising approach not only for quality purposes but also for geographic origin assignment. Many techniques can be applied for sample analysis: chromatography, mass spectrometry, and NMR have been explored. The data collected is further sorted by multivariate analysis to identify similar characteristics among the samples, reduce dimensionality and/or even propose a model for predictive purposes. This review focuses on the evolution of metabolomic profiling for the geographic origin assessment of roasted and green coffee beans in the last 21 years, the techniques that are usually applied for sample analysis and also the most common approaches for the multivariate analysis of the collected data. The prospect of applying a wide range of analytical techniques is becoming an unbiased approach to determine the origin of different roasted and green coffee beans samples with great correlation. Predictive models worked accurately for the geographic assignment of unknown samples once the variety was known.

Indexed as

Coffee beans metabolomic profilingFood metabolomicsGeographic origin assignmentInstrumental techniquesMultivariate data analysis

Identifiers

PMID38028010
PMCPMC10651463
OpenAlexW4388024723

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.